使用 Dplyr 过滤或子集 R 中的行

在本文中，我们将使用 Dplyr 包从 R 编程语言中的数据帧中过滤行。

使用中的数据框：

方法 1：使用 filter() 对行进行子集或过滤

要过滤或子集行，我们将使用 filter()函数。

语法：

filter(dataframe,condition)

这里，dataframe是输入的dataframe，condition用于过滤dataframe中的数据

示例：过滤数据框的R程序

R

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
#display actual  dataframe
print(data)
print("==========================")
  
#filter dataframe with department is sales
print(filter(data,department=="sales"))

输出：

方法 2：过滤具有多个条件的数据框

我们将使用 filter函数来过滤行。这里我们必须在过滤函数指定条件。

句法：

filter(dataframe,condition1condition2,.condition n)

这里，dataframe 是输入数据帧，条件用于过滤数据帧中的数据

示例：过滤多行的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# filter dataframe with department is sales and 
# salary is greater than 27000
print(filter(data,department=="sales" & salary >27000))

输出：

示例：按 OR运算符过滤行

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# filter dataframe with department is IT or salary 
# is greater than 27000
print(filter(data,department=="IT" |  salary >27000))

输出：

示例： R 程序使用和、或进行过滤

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# filter dataframe with department is sales 
# and salary is greater than 27000 or salary
# less than 5000
print(filter(data,department=="sales" & salary >27000 | salary<5000))

输出：

方法三：使用 slice_head()函数

此函数用于从数据框中获取前 n 行。

语法：

dataframe %>% slice_head(n)

其中，dataframe 是输入数据帧，%>% 是加载数据帧的运算符（管道运算符），n 是要显示的行数。

示例：使用 slice_head() 过滤行的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# display top 3 values with slice_head
data %>% slice_head(n=3)
print("==========================")
  
# display top 5 values with slice_head
data %>% slice_head(n=5)
print("==========================")
  
# display top 1 value  with slice_head
data %>% slice_head(n=1)

输出：

方法四：使用 slice_tail()函数

此函数用于从数据框中获取最后 n 行

语法：

dataframe %>% slice_tail(n)

其中，dataframe 是输入数据帧，%>% 是加载数据帧的运算符（管道运算符），n 是从上次开始显示的行数

示例： R 程序使用 slice_tail() 方法过滤最后一行

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# display last 3 values with slice_tail
data %>% slice_tail(n=3)
print("==========================")
  
  
# display last 5 values with slice_tail
data %>% slice_tail(n=5)
print("==========================")
  
# display last 1 value  with slice_tail
data %>% slice_tail(n=1)

输出：

方法五：使用top_n()函数

此函数用于获取前 n 行。

语法：

data %>% top_n(n=5)

示例：使用 top_n()函数过滤行的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,78900.00,
                         25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# display last 3 values with top_n
data %>% top_n(n=3)
print("==========================")
  
# display last 5 values with top_n
data %>% top_n(n=5)
print("==========================")
  
# display last 1 value  with top_n
data %>% top_n(n=1)

输出：

方法六：使用 slice_sample()函数

在这里，我们将使用 slice_sample()函数过滤行，这将随机返回样本 n 行

语法：

slice_sample(n)

示例： R 程序使用 slice_sample()函数过滤行

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# display last 3 values with slice_sample
data %>% slice_sample(n=3)
print("==========================")
  
# display last 5 values with slice_sample
data %>% slice_sample(n=5)
print("==========================")
  
# display last 1 value  with slice_sample
data %>% slice_sample(n=1)

输出：

方法七：使用 slice_max()函数

此函数基于列返回数据帧的最大 n 行

句法：

dataframe %>% slice_max(column, n )

其中数据帧是输入数据帧，列是数据帧列，其中基于此列返回最大行数，n 是要返回的最大行数

示例：使用 slice_max()函数过滤的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# return top 3 maximum rows based on salary 
# column in the dataframe
print(data %>% slice_max(salary, n = 3))
print("==========================")
  
# return top 5 maximum rows based on department 
# column in the dataframe
print(data %>% slice_max(department, n = 5))
print("==========================")

输出：

方法八：使用 slice_min()函数

此函数基于列返回数据帧的最小 n 行

句法：

dataframe %>% slice_min(column, n )

其中数据帧是输入数据帧，列是数据帧列，其中基于此列返回最大行数，n 是要返回的最小行数

示例：使用 slice_min() 过滤的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# return top 3 minimum rows based on salary 
# column in the dataframe
print(data %>% slice_min(salary, n = 3))
print("==========================")
  
# return top 5 minimum rows based on department 
# column in the dataframe
print(data %>% slice_min(department, n = 5))
print("==========================")

输出：

方法 9：使用 sample_frac()函数

sample_frac()函数从数据框（或表）中随机选择 n 个百分比的行。第一个参数包含数据框名称，第二个参数告诉要选择的行的百分比

句法：

(sample_frac(dataframe,n)

其中 dataframe 是输入数据帧，n 是分数值

示例：使用 sample_frac()函数过滤数据的 R 程序

电阻

# load the package
library(dplyr)
  
# create the dataframe with three columns
# id , department and salary with 8 rows
data=data.frame(id=c(7058,7059,7060,7089,7072,7078,7093,7034),
                  
                department=c('IT','sales','finance','IT','finance',
                             'sales','HR','HR'),
                  
                salary=c(34500.00,560890.78,67000.78,25000.00,
                         78900.00,25000.00,45000.00,90000))
  
# display actual  dataframe
print(data)
print("==========================")
  
# return 2 rows
print(sample_frac(data,0.2))
print("==========================")
  
# return 4 rows
print(sample_frac(data,0.4))
print("==========================")
  
# return 7 rows
print(sample_frac(data,0.7))
print("==========================")

输出：